1 January 2006 Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method
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J. of Electronic Imaging, 15(1), 013018 (2006). doi:10.1117/1.2179436
Abstract
We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boosting, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detecting anomalies under manufacturer production, as well as in classifying the anomalies among 20 listed categories. Manufacturer specifications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is addressed by using a classification system relying on real-time machine vision. To fulfill both real-time and quality constraints, three classification algorithms and a tree-based classification method are compared. The first one, hyperrectangle based, proves to be well adapted for real-time constraints. The second one is based on the Adaboost algorithm, and the third one, based on SVM, has a better power of generalization. Finally, a decision tree allowing improving classification performances is presented.
Johel Miteran, S. Bouillant, Michel Paindavoine, Fabrice Meriaudeau, Julien Dubois, "Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method," Journal of Electronic Imaging 15(1), 013018 (1 January 2006). https://doi.org/10.1117/1.2179436
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